Published

How One Enterprise Lifted AI Proficiency 8% to 50%

An enterprise L&D team bottled change management into Prof AI, a coaching tool that certifies staff on prompting — pushing proficiency 8% to 50% and lifting team output 10–25%.

8% → 50%+

Reached in 12 months, firm-wide

4–6 months

Implementation Time

Not disclosed

Project Cost

the challenge

Large enterprises have invested heavily in AI tools like Microsoft Copilot and ChatGPT, yet only 8–9% of knowledge workers are genuinely AI-proficient. Deployment has stalled due to employee anxiety driven by media narratives, constrained or underfunded AI licenses, lack of cultural buy-in, absence of a clear organizational AI point of view, and insufficient training — resulting in poor ROI on AI investments and growing risk of enterprise client churn for AI vendors.

what they built

Section delivers end-to-end AI workforce transformation: developing AI manifestos to establish cultural buy-in, upgrading licenses to best-in-class enterprise AI tools, running change management programs (Lunch & Learns, AI shoutouts, dedicated Slack channels), and deploying Prof AI — a proprietary AI coaching tool that certifies employees on prompting, use cases, and AI safety at scale. Section also leads hands-on workflow redesigns, identifying and rebuilding the 3–5 most impactful workflows per team with AI embedded. For very large organizations, Section recommends a 100-person pilot before full rollout.

Section's approach begins with the organizational layer: developing an AI manifesto that establishes a clear, shared point of view on AI's role, countering the media-driven anxiety that stalls adoption. License infrastructure is upgraded to best-in-class enterprise tools — ChatGPT Enterprise, Microsoft Copilot, Google Gemini, Claude, or Perplexity — based on organizational fit. Change management runs in parallel: Lunch & Learns, AI shoutout channels on Slack, and internal communications that normalize experimentation. Prof AI, Section's proprietary coaching platform, then certifies employees on prompting, use cases, and responsible AI use at scale. The final layer is workflow redesign: Section works with each team to identify and rebuild the 3–5 most impactful workflows with AI embedded directly into the process. For very large organizations, Section recommends a 100-person pilot before full rollout. Across engagements, this approach consistently moves enterprises from 8–9% AI proficiency at baseline to 50%+ meaningful usage within 12 months — creating measurable ROI on existing AI investments and competitive differentiation.

best fit for

Enterprise organizations (500+ employees) with low AI adoption despite having deployed tools; Heads of AI/Innovation or functional leaders in product and marketing responsible for AI adoption; companies in language-intensive functions (agencies, media, CPG, legal, financial services) seeking competitive AI readiness. Not industry-specific.

Ai ROLE

AI tools — including ChatGPT Enterprise, Microsoft Copilot, Google Gemini, Claude, and Perplexity — are deployed as the hands-on productivity layer for enterprise knowledge workers. Prof AI, Section's proprietary coaching tool, serves as the AI system responsible for certifying employees on prompting, use cases, and AI safety at scale.

impact

From 8% to 50%+ AI Proficiency in 12 Months

Section's approach consistently moves enterprise workforces from early-adopter levels (8–9% proficiency) to 50%+ meaningful usage within a year, creating measurable ROI and competitive differentiation for organizations committed to transformation.

Productivity Gains of 10–25% in Language-Intensive Teams

Workflow redesigns targeting the most repetitive and complex knowledge-work tasks — in teams like content marketing, brand management, and product development — deliver 10–25% productivity improvements, freeing employees from busy work and enabling higher-value contributions.

AI Board Prep Matches Human Board Input at 85–90% Accuracy

Greg's personal practice of feeding board pre-reads to four AI personas (aggressive growth, conservative, CFO-minded, etc.) consistently aligns 85–90% with what human board members actually raise in meetings — demonstrating AI's value as an executive-level thought partner, not just an operational tool.

implementation complexity

The core delivery is a structured change management program built on existing enterprise AI tools, requiring meaningful organizational coordination — AI manifestos, license management, training programs, Slack channels, and workflow redesign sprints — but no custom model development. Complexity lies in change management scale, not technical engineering.

Greg Shove

CEO @ Section | founder @ Machine and Partners | new essays every week @ personalmath.substack.com

Section

CEO of Section, Greg Shove helps enterprises drive real AI adoption. Serial founder, ex-AOL & Apple, and creator of ProFAI—an AI coach for workforce transformation and upskilling at scale.

Get an intro

Talk to this team

industry

Professional Services

business organization

HR & People

Executive & Strategy

AI TYpe

AI Workforce Enablement

value type

Revenue Growth

Time Savings

Headcount Avoidance

other(firm) Case studies

frequently asked questions

How did a large enterprise lift AI proficiency from 8% to 50% with workforce enablement?

The experts started at the organizational layer, developing an AI manifesto to set a shared point of view, then upgraded licenses to enterprise-grade tools and ran change management in parallel. A proprietary coaching platform certified employees on prompting and responsible use, and teams rebuilt their 3–5 most impactful workflows with AI embedded. This consistently moved the workforce from 8–9% proficiency to 50%-plus meaningful usage within 12 months.

What AI tools and models were used for the enterprise enablement program?

The program upgraded licenses to best-in-class enterprise tools, choosing among ChatGPT Enterprise, Microsoft Copilot, Google Gemini, Claude, and Perplexity based on fit, and deployed a proprietary AI coaching platform (Prof AI) to certify employees at scale. The approach centered on end-to-end AI workforce enablement.

What results did the enterprise achieve?

AI proficiency moved from 8% to 50%-plus meaningful usage within 12 months, workflow redesigns delivered 10–25% productivity gains in language-intensive teams, and an AI board-prep practice aligned 85–90% with what human board members actually raised.

How long did the enablement program take?

About 6–12 months to move a workforce from early-adopter levels to 50%-plus meaningful usage.

Who is this AI workforce-enablement approach best for?

Enterprise organizations of 500-plus employees with low AI adoption despite having deployed tools, and Heads of AI, Innovation, product, or marketing responsible for adoption, especially in language-intensive functions like agencies, media, CPG, legal, and financial services.

Have a similar challenge?

Ask whether this would work for you, or describe what you're trying to solve.

TELL US WHAT YOU'RE EXPLORING